MLS-C01 Modeling Practice Question
A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?
⚠ Common exam trap
Test-takers frequently confuse scaling out (Option B) or scaling up (Option D) with latency reduction, but these primarily address throughput or resource contention, not the per-request inference time on a single instance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable SageMaker Neo to compile the model for the target instance
SageMaker Neo optimizes trained models for the target hardware platform by compiling them into an efficient runtime. This reduces inference latency without changing the model architecture, making it ideal for meeting the sub-100ms requirement when the current latency is 150ms on an ml.c5.xlarge instance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of trees in the XGBoost model
Why it's wrong here
Reducing trees may reduce latency but at the cost of model accuracy; Neo is a better approach.
- ✗
Deploy multiple instances behind a load balancer
Why it's wrong here
Load balancing spreads requests but doesn't reduce per-request latency.
- ✓
Enable SageMaker Neo to compile the model for the target instance
Why this is correct
Neo optimization can reduce inference latency by optimizing the model for the hardware.
- ✗
Use a larger instance type to increase compute capacity
Why it's wrong here
Larger instances may improve throughput but not necessarily per-request latency; network overhead can increase latency.
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